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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 丁肇隆 | |
dc.contributor.author | Jen-Hsin Yang | en |
dc.contributor.author | 楊任芯 | zh_TW |
dc.date.accessioned | 2021-06-17T00:29:47Z | - |
dc.date.available | 2017-03-19 | |
dc.date.copyright | 2012-03-19 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-02-13 | |
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Conf. on Audio-and Video-Based Biometric Person Authentication(AVBP01), pp. 235-240, 2001. [8] “Federal Bureau of Investigation,” The Science of Fingerprints:Classification and Uses, Washington DC, 1984. [9] A. K. Jain, Y. Chen and M. Demirkus, “Pores and Ridges: High Resolution Fingerprint Matching Using Level 3 Features,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, NO. 1, 15-27, 2007. [10] ANSI/NIST - CDEFFS group, “Data Format for the Interchange of Extended Fingerprint and Palmprint Feature – Addendum to ANSI/NIST-ITL 1-2007,” ANSI/NIST, Working Draft 0.2, 2008, available at: http://fingerprint.nist.gov/standard/cdeffs (accessed 27 November 2008) [11] D. Maltoni, D. Maio, A. K. Jain, S. Prabhakar, “Handbook of Fingerprint Recognition,” 2009 [12] D. McMahon, G. L. Johnson, S. L. Teeter and C. G. Whitney, “A hybrid optical computer processing technique for fingerprint identification,” IEEE Transaction Computer, vol. C-24, no. 4, pp. 358-369, 1975. [13] A. Bal, A. M. El-Saba and M. S. Alam, “Enhanced fingerprint verification and identification using a Widrow cellular neural network,” Optical Engineering, vol. 44, no. 3, 2005b. [14] A. Bishnu, S. Das, S. C. Nandy and B. B. Bhattacharya, “Simple Algorithm for Partial Point Set Pattern Matching under Rigid Motion,” Pattern Recognition, vol. 39, no. 9, pp. 1662-1671, 2006. [15] G. Stockman, S. Kopstein and S. Benett, “Matching Images to Models for Registration of and Object Detection Via Clustering,” IEEE Transactions on Pattern Analysis Matching Intelligence, vol. 4, no. 3, pp. 229-241, 1982. [16] W. Sheng, G. Howells, M. C. Fairhurst and F. Deravi, “A Memetic Fingerprint Matching algorithm,” IEEE Transactions on Information Forensics and Security, vol. 2, no. 3, pp. 402-412, 2007. [17] C. Wen and T. Guo, “An efficient algorithm for fingerprint matching based on convex hulls,” International Conference on Computational Intelligence and Natural Computing, vol. 1, pp. 66-69, 2009. [18] C. Wang, M. Gavrilova, Y. Luo and J. Rokne, “An efficient algorithm for fingerprint matching,” IEEE International Conference on pattern Recognition, vol. 1, pp. 1034-1037, 2006. [19] N. Polikarpova, “On The Fractal Features in Fingerprint Analysis,” in Proc. Int. Conf. on pattern Recognition(13th), 1996. [20] J. Qi, and Y. Wang, “ A Robust Fingerprint Matching Method,” Pattern Recognition, vol. 38, no. 10, pp. 1665-1671, 2005. [21] C. P. Ling, “Software Development on Automatic Fingerprint Identification Systems,” M.S. Thesis, National Tsing Hua University, 2010. [22] A.M. Bazen and S.H. Gerez, “Segmentation of Fingerprint Images,” ProRISC 2001 Workshop on Circuits, Systems and Signal Processing, Veldhoven, The Netherlands, Nov. 2001. [23] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Trans. System, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66,1979. [24] Gonzalez, Woods, “Digital Image Processing, 3rd ed.”, 2008. [25] 李濠欣, “即時相機校正之前車偵測”, 國立台灣大學工程科學及海洋工程學系, 碩士論文, 2009. [26] 林昆緯, “車牌定位測速系統”, 國立台灣大學工程科學及海洋工程學系, 碩士論文, 2011. [27] L.Lam, S.W. Lee, and C.Y. Suen, ”Thinning Methodologies-A Comprehensive Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 9, September 1992, page 879, bottom of first column through top of second column. [28] McCabe R.M., “Fingerprint interoperability standards,” in Automatic Fingerprint Recognition Systems, N. Ratha and R. Bolle (Eds.), springer, New York, pp. 433-451, 2004. [29] N.K. Ratha, S. Chen and A.K. Jain, “Adaptive Flow Orientation-Based Feature Extraction In Fingerprint Images,” Pattern Recognition, Vol. 28, No. 11, pp. 1657-1672, 1995. [30] ANSI/NIST, “Data Format for the Interchange of Fingerprint Facial, & Other Biometric Information (revision of ANSI/NIST–ITL 1–2000),” ANSI/NIST–ITL 1–2007; NIST Special Public Report: 500–271, American National Standards Institute, 2007. [31] A.M. Bazen and S.H. Gerez, “Systematic Methods for the Computation of the Directional Fields and Singular Point of Fingerprints,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 905-919, 2002. [32] M.Kawagoe and A.Tojo, “Fingerprint Pattern Classification,” Pattern Recognition, Vol. 17, No. 3, pp. 295-303, 1984. [33] C.L. Wilson, G.T. Candela and C.I. Watson, “Neural Network Fingerprint Classification,” Journal of Artificial Neural Networks, vol. 1, no. 2, pp. 203-228, 1994. [34] K.C. Li, “Towards Building A fingerprint-Based Verification Sysetm,” M.S. Thesis, National Tsing Hua University, 2008. [35] http://bias.csr.unibo.it/fvc2002/ [36] http://www.aiontech.com.tw/cht/pdf/DS_4500%20Reader_11-08_TC.pdf [37] NEUROtechnology http://www.neurotechnology.com/ | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/66311 | - |
dc.description.abstract | 指紋辨識是目前最廣泛使用的生物辨識方法之一,常應用於使用者的安全認證,例如:指紋門禁機、機密資料的管理。本論文提出一套自動指紋辨識的方法,包含了指紋影像前處理、指紋特徵點擷取、錯誤特徵點移除、指紋分類以及指紋特徵點比對。一般而言,指紋影像前處理都會對指紋灰階影像做影像加強,期望藉由較好的強化影像而得到正確的特徵點。本論文是基於原影像直接做處理,並未先做影像加強,而是利用Brightness Filter得到較好的二值化影像,以及在錯誤特徵點移除的步驟時制定一套演算法進而得到正確的特徵點。經實驗結果顯示,即便未做灰階影像加強,也能得到正確的特徵點,與知名的生物辨識研究團隊NEUROtechnology所擷取到特徵點比較,本論文的效果也相當不錯。 | zh_TW |
dc.description.provenance | Made available in DSpace on 2021-06-17T00:29:47Z (GMT). No. of bitstreams: 1 ntu-101-R98525042-1.pdf: 6506952 bytes, checksum: 0629d46cff81667cca1166fd7193e6d8 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 口試委員審定書 i
致謝 ii 中文摘要 iii ABSTRACT iv 論文目錄 v 圖目錄 vii 表目錄 x Chapter 1 緒論 1 1.1 研究動機與目的 1 1.2 相關研究 1 1.2.1 指紋影像擷取 1 1.2.2 指紋影像前處理 2 1.2.3 指紋特徵的定義及種類 2 1.2.4 特徵點比對 4 1.3 論文貢獻 4 1.4 論文架構 5 Chapter 2 指紋影像前處理 6 2.1 指紋區域偵測 6 2.1.1 區塊大小及門檻值T的制定 7 2.2 二值化 10 2.2.1 不同的二值化方法 10 2.2.2 Brightness Filter 16 2.3 修補二值化所產生的空洞 17 2.4 細線化 19 Chapter 3 特徵點擷取與除錯 22 3.1 特徵點擷取 22 3.1.1 特徵點 22 3.1.2 特徵點擷取 22 3.2 修補細線化紋線 23 3.2.1 根據分岔點修補細線化紋線 25 3.2.2 根據終端點修補細線化 30 3.3 除去錯誤特徵點 31 3.4 特徵點的表示 32 Chapter 4 指紋分類與比對 34 4.1 方向場計算 34 4.2 奇異點偵測 37 4.3 指紋分類 38 4.4 指紋比對 41 4.4.1 座標轉換 41 4.4.2 指紋比對 43 Chapter 5 實驗結果與討論 45 5.1 裝備設置與使用的資料庫 45 5.2 特徵點擷取的結果 46 5.3 系統流程實作與實驗結果 48 5.3.1 系統流程 48 5.3.2 系統實作 50 5.3.3 資料庫Lab125a指紋影像擷取及特徵點擷取之討論 55 5.3.4 與NEUROtechnology設計的SDK做特徵點比較 59 5.3.5 對資料庫FVC2002 DB1做指紋比對結果 62 5.3.6 本系統目前限制 64 Chapter 6 結論與未來方向 65 參考文獻 66 | |
dc.language.iso | zh-TW | |
dc.title | 指紋辨識 | zh_TW |
dc.title | Fingerprint Recognition | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 張瑞益 | |
dc.contributor.oralexamcommittee | 張恆華,黃乾綱 | |
dc.subject.keyword | 指紋辨識,特徵點除錯,指紋分類,指紋比對,Brightness Filter, | zh_TW |
dc.subject.keyword | Fingerprint Identification,Spurious Minutiae Removing,Brightness Filter,Fingerprint Classification, | en |
dc.relation.page | 68 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-02-13 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
顯示於系所單位: | 工程科學及海洋工程學系 |
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